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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9349593495934959
- name: Recall
type: recall
value: 0.9468562874251497
- name: F1
type: f1
value: 0.9408702119747119
- name: Accuracy
type: accuracy
value: 0.9490662139219015
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2730
- Precision: 0.9350
- Recall: 0.9469
- F1: 0.9409
- Accuracy: 0.9491
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.17 | 250 | 1.0147 | 0.7119 | 0.7807 | 0.7447 | 0.7963 |
| 1.3916 | 8.33 | 500 | 0.5211 | 0.8428 | 0.8705 | 0.8564 | 0.8786 |
| 1.3916 | 12.5 | 750 | 0.3842 | 0.8961 | 0.9169 | 0.9064 | 0.9181 |
| 0.3265 | 16.67 | 1000 | 0.3158 | 0.9225 | 0.9349 | 0.9286 | 0.9393 |
| 0.3265 | 20.83 | 1250 | 0.2874 | 0.9162 | 0.9334 | 0.9247 | 0.9414 |
| 0.139 | 25.0 | 1500 | 0.2738 | 0.9255 | 0.9394 | 0.9324 | 0.9461 |
| 0.139 | 29.17 | 1750 | 0.2774 | 0.9354 | 0.9431 | 0.9392 | 0.9491 |
| 0.0798 | 33.33 | 2000 | 0.2695 | 0.9342 | 0.9461 | 0.9401 | 0.9508 |
| 0.0798 | 37.5 | 2250 | 0.2759 | 0.9356 | 0.9461 | 0.9408 | 0.9495 |
| 0.0592 | 41.67 | 2500 | 0.2730 | 0.9350 | 0.9469 | 0.9409 | 0.9491 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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